For UK organisations that handle sensitive data
Are your staff pasting customer emails into public AI tools like ChatGPT, Gemini, and Claude?
Under UK law, that's a problem.
So I design and develop private AI systems instead. These are assistants that run inside your own infrastructure, where your team get the AI they want and nothing leaves your organisation.
AI is probably already in your business.
Whatever your policy says, people on your team are likely using AI to get through their work. Maybe with ChatGPT, maybe Gemini, maybe Claude. It's possible your people are pasting client emails into these tools for a quick redraft. Summarising long contracts. Cleaning up customer notes. None of this is reckless. Just useful.
The productivity gains are obvious. But there's a downside.
If a regulator or a client asked what data went where, your organisation might not have a clean answer.
Why that's a problem
When a staff member pastes a client document into ChatGPT, that document goes to a US company. But under UK law, the responsibility for what happened with that data sits with your organisation, not the staff member who pasted it.
This is not hypothetical. Engineers at Samsung pasted confidential source code into ChatGPT to check it, and that code left the company's control. One study found more than one in ten of the things employees paste into these tools is confidential.
Your data (and maybe that of your clients and customers) is somewhere you can't see, in a system you don't control, sitting in logs you can't audit.
This has a name. When people use AI tools your organisation never approved, that's Shadow AI.
"Working with Peter was an absolute pleasure, thanks to his flexibility, excellent communication skills and honesty."
Luca Russo, Web & Social Collaboration Functional Lead, Givaudan SA
The private AI approach
The good news: none of this means your team has to stop using AI. The risk comes from where the data goes, not from the AI itself.
Private AI is the way around that.
It isn't a product you can buy. It's a way of setting an AI tool up so it runs inside your own space, on data that stays with you. Your team get the same kind of assistant they would get from ChatGPT, but your questions and answers never leave your control, and you can see what it does and switch it off whenever you want.
A lot of what gets sold as private AI stops at where the data is stored and leaves the rest open.
Here's a fuller walkthrough of what makes an AI tool genuinely private, and what falls over when part of it is missing.
What a private AI build can do
That's the idea. Here's what it looks like once it's built.
The technical part lives in your cloud. What your team sees is closer to the chat tools they're already used to.
Six capabilities cover the shape of what's possible: where your data ends up, whose rules the assistant follows, how it sticks to your own facts instead of making things up, how it reasons across many of your documents at once, how it connects to the systems you already use, and how it keeps working as AI tools change.
Each capability is illustrated with a short clip from a real build.
How we'd work together
The Discovery. Most engagements start here: a fixed price two week scope of what your team is already doing with AI, what they'd actually want to do with it, what state your data is in, and what's feasible to build first.
You get a written report covering the use cases worth pursuing, the data work involved, and an architecture sketch for a first build. No commitment to go further.
What happens after the Discovery depends on what it finds. Usually it's one or both of two phases.
The data work. More often than not, the data isn't ready. It's spread across systems, stored in formats that are awkward to use, or nobody's quite sure which of it you actually need. Getting it into shape is usually the larger piece of the job, and it's the part that decides whether everything built on top of it holds up.
The private AI build. Then the tools themselves: the AI assistants your staff use day to day, built and run inside your own infrastructure. This is the part the use cases above show.
"Pete has been amazing to work with. He explains things to us in a way that makes sense in English, not developer-speak. We feel like Pete is an extended part of our team."
Katie Angotti, Programme Lead, Danone
About me
I've spent over 25 years building the data architecture under big software for UK organisations.
The platforms have changed (Perl and Python before the web was the default, Drupal and Laravel through the 2010s, private AI today) but the work hasn't. It's still about getting messy data out of legacy systems and into shape so something useful can sit on top.
Since 2009 I've done that for over 45 UK organisations. Private AI is just the new layer on top. The data plumbing underneath is still the hard part.
A few you may recognise:
"We have worked with many developers and can confidently say that Pete is by far the best, an exceptional coder and consultant with an impressive skill set."
Kathryn Maxwell, IT Project Manager, Royal Meteorological Society